Movies on Streaming Platforms

Data Cleaning

library(readr)
#Data set found at:
#https://www.kaggle.com/ruchi798/movies-on-netflix-prime-video-hulu-and-disney
#Updated May 22, 2020
#16744 total movies in data set
movdata <- read_csv("MoviesOnStreamingPlatforms_updated.csv", 
    col_types = cols(X1 = col_skip(), Year = col_integer()))

## Warning: Missing column names filled in: 'X1' [1]

#rename variables in data set for easier use
names(movdata)[6] <- "RottenTomatoes"
names(movdata)[9] <- "PrimeVideo"
names(movdata)[10] <- "Disney"

#change variables to factor
movdata$Year <- as.factor(movdata$Year)
movdata$Age <- as.factor(movdata$Age)
movdata$Netflix <- as.factor(movdata$Netflix)
movdata$Hulu <- as.factor(movdata$Hulu)
movdata$PrimeVideo <- as.factor(movdata$PrimeVideo)
movdata$Disney <- as.factor(movdata$Disney)

#rename Age level all to All, and Platform levels to Yes or No
library(plyr)
movdata$Age <- revalue(movdata$Age, c("all"="All"))
movdata$Netflix <- revalue(movdata$Netflix, c("0"="No", "1"="Yes"))
movdata$Hulu <- revalue(movdata$Hulu, c("0"="No", "1"="Yes"))
movdata$PrimeVideo <- revalue(movdata$PrimeVideo, c("0"="No", "1"="Yes"))
movdata$Disney <- revalue(movdata$Disney, c("0"="No", "1"="Yes"))
#rename age levels to more known language
movdata$Age <- revalue(movdata$Age, c("All"="G", "7+"="PG", "13+"="PG-13", "16+"="TV-14", "18+"="R")) 

#Order Age groups from young to old
movdata$Age <- ordered(movdata$Age, levels = c("G", "PG", "PG-13", "TV-14", "R"))

#Remove % symbol from Rotten Tomatoes variable and change to numeric
movdata$RottenTomatoes <- gsub("%", "", movdata$RottenTomatoes)
movdata$RottenTomatoes <- as.numeric(movdata$RottenTomatoes)

#remove Type column
movdata <- subset(movdata, select = -c(11))

#Show first 6 rows of dataset
head(movdata[1:10])

## # A tibble: 6 x 10
##      ID Title   Year  Age    IMDb RottenTomatoes Netflix Hulu  PrimeVideo Disney
##   <dbl> <chr>   <fct> <ord> <dbl>          <dbl> <fct>   <fct> <fct>      <fct> 
## 1     1 Incept… 2010  PG-13   8.8             87 Yes     No    No         No    
## 2     2 The Ma… 1999  R       8.7             87 Yes     No    No         No    
## 3     3 Avenge… 2018  PG-13   8.5             84 Yes     No    No         No    
## 4     4 Back t… 1985  PG      8.5             96 Yes     No    No         No    
## 5     5 The Go… 1966  R       8.8             97 Yes     No    Yes        No    
## 6     6 Spider… 2018  PG      8.4             97 Yes     No    No         No

head(movdata[11:15])

## # A tibble: 6 x 5
##   Directors              Genres             Country        Language      Runtime
##   <chr>                  <chr>              <chr>          <chr>           <dbl>
## 1 Christopher Nolan      Action,Adventure,… United States… English,Japa…     148
## 2 Lana Wachowski,Lilly … Action,Sci-Fi      United States  English           136
## 3 Anthony Russo,Joe Rus… Action,Adventure,… United States  English           149
## 4 Robert Zemeckis        Adventure,Comedy,… United States  English           116
## 5 Sergio Leone           Western            Italy,Spain,W… Italian           161
## 6 Bob Persichetti,Peter… Animation,Action,… United States  English,Span…     117

attach(movdata)

Column Descriptions

Title: Title of Movie
Year: Year in which the Movie was released (1902-2020)
Age: Target Age Group (G, PG, PG-13, TV-14, R)
IMDb: IMDb rating (0-10)
RottenTomatoes: Rotten Tomatoes percentage rating (0-100)
Netflix: Whether the movie is found on Netflix (Yes or No)
Hulu: Whether the movie is found on Hulu (Yes or No)
PrimeVideo: Whether the movie is found on Prime Video (Yes or No)
Disney: Whether the movie is found on Disney+ (Yes or No)
Directors: Lists the directors of the movie
Genres: Lists the genres of the movie
Country: Lists the countries the movie is available in
Language: Lists the languages the movie is available in
Runtime: Length of movie in minutes

Summary of Data

#Show summary of data
#56.1% missing Age data
#3.4% missing IMDb data
#69.2% missing RottenTomatoes data
#1.6% missing Genres data (275)
#4.3% missing Directors data (726)
#3.5% missing Runtime data
summary(movdata)

##        ID           Title                Year         Age            IMDb      
##  Min.   :    1   Length:16744       2017   :1401   G    : 843   Min.   :0.000  
##  1st Qu.: 4187   Class :character   2018   :1285   PG   :1462   1st Qu.:5.100  
##  Median : 8372   Mode  :character   2016   :1206   PG-13:1255   Median :6.100  
##  Mean   : 8372                      2015   :1065   TV-14: 320   Mean   :5.903  
##  3rd Qu.:12558                      2014   : 986   R    :3474   3rd Qu.:6.900  
##  Max.   :16744                      2013   : 964   NA's :9390   Max.   :9.300  
##                                     (Other):9837                NA's   :571    
##  RottenTomatoes   Netflix      Hulu       PrimeVideo  Disney     
##  Min.   :  2.00   No :13184   No :15841   No : 4390   No :16180  
##  1st Qu.: 44.00   Yes: 3560   Yes:  903   Yes:12354   Yes:  564  
##  Median : 71.00                                                  
##  Mean   : 65.43                                                  
##  3rd Qu.: 88.00                                                  
##  Max.   :100.00                                                  
##  NA's   :11586                                                   
##   Directors            Genres            Country            Language        
##  Length:16744       Length:16744       Length:16744       Length:16744      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##     Runtime       
##  Min.   :   1.00  
##  1st Qu.:  82.00  
##  Median :  92.00  
##  Mean   :  93.41  
##  3rd Qu.: 104.00  
##  Max.   :1256.00  
##  NA's   :592

Directors

#highest- Jay Chapman 
  #All around 60 minute comedies on Netflix or Prime Video
library(ggplot2)
library(tidyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

dirdata <- movdata[complete.cases(movdata$Directors),] #data set without missing Directors - 16018 obs.
dirfreq <- count(dirdata, Directors) #freq of Directors
dirfreq$rank <- rank(-dirfreq$n,ties.method="min")
dirfreq <- dirfreq[order(dirfreq$rank,decreasing = F),]
top10dira <- dirfreq[dirfreq$rank < 11,]
#Top 10 Genre groups
dir_10 <- ggplot(top10dira, aes(x=reorder(Directors,-n), y=n, fill=Directors))+
  xlab("Directors")+
  ylab("Number of Movies")+
  ylim(0, 40)+
  ggtitle("Top 10 Director Groups")+
  geom_text(aes(label=n), vjust = -.5) +
  theme(legend.position = "none", axis.text = element_text(size=12), plot.title = element_text(hjust=0.5),
        axis.text.x = element_text(angle = 55, vjust=0.95, hjust = 0.95))+
  geom_bar(stat="identity", width=0.75)
dir_10

#12453 different directors
#9540 only directed 1 movie - 76.6%
#1749 directed 2 movies - 14%
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
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##     between, first, last

data0 <- movdata[,c(1,11)] #data set with movie ID and directors
data0 <- data0[complete.cases(data0$Directors),] #remove NAs
dt1 <- as.data.table(data0) #change to data table
#split the Directors by commas and create another row from the split with same ID
directors1 <- setDT(dt1)[,lapply(.SD, function(x) unlist(tstrsplit(x, ",",fixed=TRUE))), by = data0$ID]
dirfreq1 <- count(directors1, Directors) #frequency of Directors
dirfreq1$rank <- rank(-dirfreq1$n,ties.method="min") #create rank column by frequency
dirfreq1 <- dirfreq1[order(dirfreq1$rank,decreasing = F),] #order by decreasing rank
top50dir <- dirfreq1[dirfreq1$rank < 51,]
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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##     last_plot

## The following objects are masked from 'package:plyr':
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## The following object is masked from 'package:stats':
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#plotly graph of directors by decreasing rank
dir_sep <- plot_ly(top50dir, x = ~reorder(Directors, rank), y = ~n, type = 'bar', name = 'Directors') %>%
  layout(xaxis = list(title = "Directors"),
         yaxis = list(title = "Number of Movies"),
         title= "Top 50 Directors on Streaming Platforms")
dir_sep

Genres

#Genres:
#Action, Adventure, Animation, Biography, Comedy, Crime, Documentary, Drama, Family, Fantasy, Film-Noir, Game-Show, History, Horror, Music, Musical, Mystery, News, Reality-TV, Romance, Sci-Fi, Short, Sport, Talk-Show, Thriller, War, Western

#create graph representing the top 10 genre groups
genredata <- movdata[complete.cases(movdata$Genres),] #data set without missing Genres - 16469 obs.
genrefreq <- count(genredata, Genres) #
genrefreq$rank <- rank(-genrefreq$n,ties.method="min")
genrefreq <- genrefreq[order(genrefreq$rank,decreasing = F),]
top10genresa <- genrefreq[genrefreq$rank < 11,]
#Top 10 Genre groups
genres_10 <- ggplot(top10genresa, aes(x=reorder(Genres,-n), y=n, fill=Genres))+
  xlab("Genres")+
  ylab("Number of Movies")+
  ylim(0, 1500)+
  ggtitle("Top 10 Genre Groups")+
  geom_text(aes(label=n), vjust = -.5) +
  theme(legend.position = "none", axis.text = element_text(size=12), plot.title = element_text(hjust=0.5),
        axis.text.x = element_text(angle = 55, vjust=0.95, hjust = 0.95))+
  geom_bar(stat="identity", width=0.75)
genres_10

#Drama - 8.1%

data1 <- movdata[,c(1,12)] #data set with movie ID and genres
data1 <- data1[complete.cases(data1$Genres),] #remove NAs
dt <- as.data.table(data1) #change to data table
#split the genres by commas and create another row from the split with same ID
genre1 <- setDT(dt)[,lapply(.SD, function(x) unlist(tstrsplit(x, ",",fixed=TRUE))), by = data1$ID]
genrefreq1 <- count(genre1, Genres) #frequency of genres
genrefreq1$rank <- rank(-genrefreq1$n,ties.method="min") #create rank column by frequency
genrefreq1 <- genrefreq1[order(genrefreq1$rank,decreasing = F),] #order by decreasing rank
#plotly graph of genres by decreasing rank
genre_sep <- plot_ly(genrefreq1, x = ~reorder(Genres, rank), y = ~n, type = 'bar', name = 'Genres') %>%
  layout(xaxis = list(title = "Genres ordered by Frequency"),
         yaxis = list(title = "Number of Movies"),
         title= "Genres")
genre_sep

#Drama- 43.9%
#Comedy - 28.2%

Which words are most often in the movie titles?

#create wordcloud of movie titles
library(wordcloud)

## Loading required package: RColorBrewer

library(tm)

## Loading required package: NLP

## 
## Attaching package: 'NLP'

## The following object is masked from 'package:ggplot2':
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library(RColorBrewer)
library(SnowballC)
titledata <- movdata$Title
docs <- Corpus(VectorSource(titledata))
docs

## <<SimpleCorpus>>
## Metadata:  corpus specific: 1, document level (indexed): 0
## Content:  documents: 16744

toSpace <- content_transformer(function (x , pattern ) gsub(pattern, "", x))
docs <- tm_map(docs, toSpace, ":")

## Warning in tm_map.SimpleCorpus(docs, toSpace, ":"): transformation drops
## documents

docs <- tm_map(docs, toSpace, "-")

## Warning in tm_map.SimpleCorpus(docs, toSpace, "-"): transformation drops
## documents

docs <- tm_map(docs, content_transformer(tolower)) #lowercase

## Warning in tm_map.SimpleCorpus(docs, content_transformer(tolower)):
## transformation drops documents

docs <- tm_map(docs, removeNumbers) #remove numbers

## Warning in tm_map.SimpleCorpus(docs, removeNumbers): transformation drops
## documents

docs <- tm_map(docs, removeWords, c("the", "at", "of", "on", 
                                    "and", "vs", "an", "for", "from", "with")) #remove common words

## Warning in tm_map.SimpleCorpus(docs, removeWords, c("the", "at", "of", "on", :
## transformation drops documents

docs <- tm_map(docs, removePunctuation) #remove punctuation

## Warning in tm_map.SimpleCorpus(docs, removePunctuation): transformation drops
## documents

docs <- tm_map(docs, stripWhitespace) #remove extra white space

## Warning in tm_map.SimpleCorpus(docs, stripWhitespace): transformation drops
## documents

dtm <- TermDocumentMatrix(docs)
m <- as.matrix(dtm)
v <- sort(rowSums(m),decreasing=TRUE)
d <- data.frame(word = names(v),freq=v)
head(d, 20)

##                word freq
## love           love  236
## story         story  205
## man             man  198
## life           life  167
## christmas christmas  157
## you             you  153
## movie         movie  131
## last           last  130
## night         night  125
## time           time  116
## dead           dead  113
## black         black  106
## girl           girl  104
## all             all  102
## death         death  100
## one             one   99
## world         world   98
## house         house   97
## american   american   95
## little       little   92

cloud <- wordcloud(words = d$word, freq = d$freq, min.freq = 1,
          max.words=50, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

Streaming Platforms

#number of movies in each platform
#No missing data in each platform
platMat <- sapply(X = movdata[7:10], FUN = table) #frequency table
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+") #names of platforms
count_no <- platMat[1:1, 1:4] #extracts frequencies of "No" values
count_yes <- platMat[2:2, 1:4] #extracts frequencies of "Yes" values
count_yesN <- count_yes[1] #total number of movies in Netflix - 3560 (21.3%)
count_yesH <- count_yes[2] #total number of movies in Hulu - 903 (5.4%)
count_yesP <- count_yes[3] #total number of movies in Prime Video - 12354 (73.8%)
count_yesD <- count_yes[4] #total number of movies in Disney - 564 (3.4%)
platdata <- data.frame(platforms, count_yes, count_no) #reorganization of freq table

#Plot number of movies in each platform as a grouped bar chart
plat <- plot_ly(platdata, x = ~platforms, y = ~count_yes, type = 'bar', name = 'Yes') %>%
  add_trace(y = ~count_no, name = 'No') %>%
  layout(title = "Number of Movies in Each Streaming Platform",
                        yaxis = list(title = 'Number of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the movie in the platform?")))
plat #plot plat

Years

#Years
#No missing values in data set
yearP <- plot_ly(movdata, x = ~Year) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per Year",
        xaxis = list(title = "Year"),
        yaxis = list(title = "Number of Movies"))
yearP

Platforms Content by Release Year

library(purrr)

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:data.table':
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## The following object is masked from 'package:plyr':
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library(dplyr)
#create data sets with counts of Movies per year for each platform
yearNetflix <- movdata %>% modify_if(is.character, as.factor) %>% group_by(Netflix, Year) %>% tally() %>% filter(Netflix == "Yes") %>% na.omit() %>% ungroup() %>% select(-Netflix) %>% 
  mutate(NetflixPer = (n / count_yesN)*100)
names(yearNetflix)[2] <- "Netflix" 

yearHulu <- movdata %>% modify_if(is.character, as.factor) %>% group_by(Hulu, Year) %>% tally() %>% filter(Hulu == "Yes") %>% na.omit() %>% ungroup() %>% select(-Hulu) %>% 
  mutate(HuluPer = (n / count_yesH)*100)
names(yearHulu)[2] <- "Hulu" 

yearPrime <- movdata %>% modify_if(is.character, as.factor) %>% group_by(PrimeVideo, Year) %>% tally() %>% filter(PrimeVideo == "Yes") %>% na.omit() %>% ungroup() %>% select(-PrimeVideo) %>% 
  mutate(PrimePer = (n / count_yesP)*100)
names(yearPrime)[2] <- "PrimeVideo" 

yearDisney <- movdata %>% modify_if(is.character, as.factor) %>% group_by(Disney, Year) %>% tally() %>% filter(Disney == "Yes") %>% na.omit() %>% ungroup() %>% select(-Disney) %>% 
  mutate(DisneyPer = (n / count_yesD)*100)
names(yearDisney)[2] <- "Disney" 

#join datasets and replace NAs with 0
yearPlatData <- full_join(yearNetflix, yearHulu, by="Year")
yearPlatData <- full_join(yearPlatData, yearPrime, by="Year")
yearPlatData <- full_join(yearPlatData, yearDisney, by="Year")
yearPlatData <- yearPlatData %>% mutate_all(~replace(., is.na(.), 0))

## Warning in `[<-.factor`(`*tmp*`, list, value = 0): invalid factor level, NA
## generated

yearPlatData$Year <- as.factor(yearPlatData$Year)
yearPlatData <- yearPlatData[order(yearPlatData$Year),] #order Years

#plot of movie count per year per platform
yearPlatPlot <- plot_ly(yearPlatData, x = ~Year, y = ~Netflix, type = 'scatter', mode = "lines", name = 'Netflix', line = list(color = "firebrick")) %>%
    add_trace(y = ~Hulu, name = 'Hulu', mode="lines", line = list(color = "#00EE76")) %>%
    add_trace(y = ~PrimeVideo, name = 'Prime Video', mode="lines", line = list(color = "#000033")) %>%
    add_trace(y = ~Disney, name = 'Disney+', mode="lines", line = list(color = "#0A47CC")) %>%
    layout(title = "Each Platform's Content Available by Release Year",
                        yaxis = list(title = 'Number of Movies'), 
                        xaxis = list(title = 'Year'),
                        legend = list(title = list(text = "Platform")))

yearPlatPlot

#plot of movie percent per year per platform
yearPlatPlotPer <- plot_ly(yearPlatData, x = ~Year, y = ~NetflixPer, type = 'scatter', mode = "lines", name = 'Netflix', line = list(color = "firebrick")) %>%
    add_trace(y = ~HuluPer, name = 'Hulu', mode="lines", line = list(color = "#00EE76")) %>%
    add_trace(y = ~PrimePer, name = 'Prime Video', mode="lines", line = list(color = "#000033")) %>%
    add_trace(y = ~DisneyPer, name = 'Disney+', mode="lines", line = list(color = "#0A47CC")) %>%
    layout(title = "Each Platform's Content Available by Release Year",
                        yaxis = list(title = 'Percent of Movies in Platform'), 
                        xaxis = list(title = 'Year'),
                        legend = list(title = list(text = "Platform")))

yearPlatPlotPer

Age Group

#Age
#9390 missing values in data set, so graph includes 7354 observations, 56.1%
ageNA <- movdata[complete.cases(movdata$Age),] #data set without missing age obs.
ageP <- plot_ly(ageNA, x = ~Age) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per Age Group",
        xaxis = list(title = "Age Group"),
        yaxis = list(title = "Number of Movies"))
ageP

Platforms by Age Group

#Count how many movies in platforms when missing ages values are taken out
platMatAgeCount <- sapply(X = ageNA[7:10], FUN = table)
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+")
count_noA <- platMatAgeCount[1:1, 1:4]
count_yesA <- platMatAgeCount[2:2, 1:4]
count_yesAN <- count_yesA[1]
count_yesAH <- count_yesA[2]
count_yesAP <- count_yesA[3]
count_yesAD <- count_yesA[4]

#Create datasets for each platform with count levels
ageNetflix <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Netflix, Age) %>% tally() %>% filter(Netflix == "Yes") %>% na.omit() %>% ungroup() %>% select(-Netflix) %>% 
  mutate(NetflixPer = (n / count_yesAN)*100)
names(ageNetflix)[2] <- "Netflix" 

ageHulu <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Hulu, Age) %>% tally() %>% filter(Hulu == "Yes") %>% na.omit() %>% ungroup() %>% select(-Hulu) %>% 
  mutate(HuluPer = (n / count_yesAH)*100)
names(ageHulu)[2] <- "Hulu" 

agePrime <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(PrimeVideo, Age) %>% tally() %>% filter(PrimeVideo == "Yes") %>% na.omit() %>% ungroup() %>% select(-PrimeVideo) %>% 
  mutate(PrimePer = (n / count_yesAP)*100)
names(agePrime)[2] <- "PrimeVideo" 

ageDisney <- ageNA %>% modify_if(is.character, as.factor) %>% group_by(Disney, Age) %>% tally() %>% filter(Disney == "Yes") %>% na.omit() %>% ungroup() %>% select(-Disney) %>% 
  mutate(DisneyPer = (n / count_yesAD)*100)
names(ageDisney)[2] <- "Disney" 

#Join datasets by Age and replace any null values to be 0
agePlatData <- full_join(ageNetflix, ageHulu, by="Age")
agePlatData <- full_join(agePlatData, agePrime, by="Age")
agePlatData <- full_join(agePlatData, ageDisney, by="Age")
agePlatData <- agePlatData %>% mutate_all(~replace(., is.na(.), 0))

## Warning in `[<-.factor`(`*tmp*`, list, value = 0): invalid factor level, NA
## generated

#create distribution of platforms for movies without missing ages
ageplatNA <- plot_ly(ageNA, x = ~platforms, y = ~count_yesA, type = 'bar', name = 'Yes') %>%
  add_trace(y = ~count_noA, name = 'No') %>%
  layout(title = "Number of Movies in Each Streaming Platform (w/o missing ages)",
                        yaxis = list(title = 'Number of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the movie in the platform?")))
ageplatNA #plot

#Create plot of count of ages by platforms
agePlat <- plot_ly(agePlatData, x = ~Age, y = ~Netflix, type = 'bar', name = 'Netflix', marker = list(color = "firebrick")) %>%
    add_trace(y = ~Hulu, name = 'Hulu', marker = list(color = "#00EE76")) %>%
    add_trace(y = ~PrimeVideo, name = 'Prime Video', marker = list(color = "#000033")) %>%
    add_trace(y = ~Disney, name = 'Disney+', marker = list(color = "#0A47CC")) %>%
    layout(title = "Number of Movies of Each Rating in Each Platform",
                        yaxis = list(title = 'Number Of Movies'), 
                        xaxis = list(title = 'Age Group'),
                        barmode = 'group',
                        legend = list(title = list(text = "Platform")))

#Create plot of percent of ages by platforms
agePlatPer <- plot_ly(agePlatData, x = ~Age, y = ~NetflixPer, type = 'bar', name = 'Netflix', marker = list(color = "firebrick")) %>%
    add_trace(y = ~HuluPer, name = 'Hulu', marker = list(color = "#00EE76")) %>%
    add_trace(y = ~PrimePer, name = 'Prime Video', marker = list(color = "#000033")) %>%
    add_trace(y = ~DisneyPer, name = 'Disney+', marker = list(color = "#0A47CC")) %>%
    layout(title = "Percent of Movies in Each Age Group for Each Platform",
                        yaxis = list(title = 'Percent of Movies in Platform'), 
                        xaxis = list(title = 'Age Group'),
                        barmode = 'group',
                        legend = list(title = list(text = "Platform")))
agePlat

agePlatPer

Runtime

#Max: 1256 min (20.9 hours)
  #Movie: Colorado
  #Platform: Prime Video
  #Year: 1940
  #IMDb: 5.9
  #Country: United States
#Next: 750 min (12.5 hours)
  #Movie: Law of the Lawless
  #Platform: Prime Video, Hulu
  #Year: 1964
  #IMDb: 6.1
  #Country: Russia
#plot all runtimes
runNA <- movdata[complete.cases(movdata$Runtime),] #data set without missing Runtime (16152 obs - 592 missing)
runP <- plot_ly(runNA, x = ~Runtime) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per Runtime",
        xaxis = list(title = "Runtime (min.)"),
        yaxis = list(title = "Number of Movies"))
runP

#Min: 1 min (Short film)
  #Movie: Liefling The Movie
  #Platform: Netflix
  #Year: 2010
  #IMDb: 6.3
  #Country: Canada

#8.5% movies of have 90 min runtimes
#plot runtimes less than 400
runNA1 <- runNA[runNA$Runtime < 400,] #data with Runtime less than 400 (16149 obs - 3 removed)
runP1 <- plot_ly(runNA1, x = ~Runtime) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per Runtime (less than 400 min)",
        xaxis = list(title = "Runtime (min.)"),
        yaxis = list(title = "Number of Movies"))
runP1

Platforms by Runtime

#Count how many movies in platforms when missing runtimes values are taken out
runNA <- as.data.frame(runNA)
platMatRunCount <- sapply(X = runNA[7:10], FUN = table)
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+")
count_noR <- platMatRunCount[1:1, 1:4]
count_yesR <- platMatRunCount[2:2, 1:4]
count_yesRN <- count_yesR[1]
count_yesRH <- count_yesR[2]
count_yesRP <- count_yesR[3]
count_yesRD <- count_yesR[4]

#plot of runtime by platform
runPlatbox <- plot_ly(data=runNA, y = ~Runtime[Netflix == 'Yes'], x = ~(Netflix=c("Yes")), type = 'box', name = 'Netflix', marker = list(color = "firebrick"), color=I("firebrick"), text = ~paste("Movie: ", runNA$Title[Netflix == "Yes"], '<br>Year:', runNA$Year[Netflix=="Yes"])) %>%
    add_trace(y = ~Runtime[Hulu == 'Yes'], x = ~(Hulu=c("Yes")),  name = 'Hulu', marker = list(color = "#00EE76"), color=I("#00EE76"), text = ~paste("Movie: ", runNA$Title[Hulu == "Yes"], '<br>Year:', runNA$Year[Hulu=="Yes"])) %>%
    add_trace(y = ~Runtime[PrimeVideo == 'Yes'], x = ~(PrimeVideo=c("Yes")), name = 'Prime Video', marker = list(color = "#000033"), color=I("#000033"), text = ~paste("Movie: ", runNA$Title[PrimeVideo == "Yes"], '<br>Year:', runNA$Year[PrimeVideo=="Yes"])) %>%
    add_trace(y = ~Runtime[Disney == 'Yes'], x = ~(Disney=c("Yes")), name = 'Disney+', marker = list(color = "#0A47CC"), color=I("#0A47CC"), text = ~paste("Movie: ", runNA$Title[Disney == "Yes"], '<br>Year:', runNA$Year[Disney=="Yes"])) %>%
    layout(title = "Runtimes for Each Platform",
                        yaxis = list(title = 'Runtimes'), 
                        xaxis = list(title = 'Platform',tickvals = c()),
                        boxmode = "group",
                        boxmean=TRUE,
                        legend = list(title = list(text = "Platform")))
runPlatbox

## Warning: 'layout' objects don't have these attributes: 'boxmode', 'boxmean'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'

IMDb Rating

#IMDb rating
#571 missing values in data, so graph includes 16173 observations
#Highest: 9.3 with 6 movies
    #do not have RottenTomatoes Ratings
    #1 on Netflix, 5 on Prime
#Lowest: 0 with 4 movies
    #do not have RottenTomatoes Ratings
    #3 in Prime, 1 in Hulu
imdbNA <- movdata[complete.cases(movdata$IMDb),] #data set without missing IMDb
imdbP <- plot_ly(imdbNA, x = ~IMDb) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per IMDb Rating",
        xaxis = list(title = "IMDb Rating"),
        yaxis = list(title = "Number of Movies"))
imdbP

#7489 movies with IMDb ratings less than 6 (46.3%)
sum(na.omit(IMDb) < 6)

## [1] 7489

#8684 movies with IMDb ratings greater than or equal to 6 (53.7%)
sum(na.omit(IMDb) >= 6)

## [1] 8684

Rotten Tomatoes Percent Rating

#Rotten Tomatoes
#11586 missing values, so graph includes 5158 observations
#Highest: 100 with 407 movies (7.9%)
    #IMDb ratings 3.6-8.6
#Lowest: 2 with 4 movies
    #IMDb ratings 4.4-5.4
    #2 in Prime, 2 in Hulu
rotNA <- movdata[complete.cases(movdata$RottenTomatoes),] #data set without NA RottenTomatoes
rotP <- plot_ly(rotNA, x = ~RottenTomatoes) %>%
  add_histogram() %>% 
  layout(title = "Number of Movies per Rotten Tomatoes Percentage",
        xaxis = list(title = "Rotten Tomatoes Percentage"),
        yaxis = list(title = "Number of Movies"))
rotP

#1906 movies with rotten tomatoes ratings less than 6 (37.0%)
sum(na.omit(RottenTomatoes) < 60)

## [1] 1906

#3252 movies with rotten tomatoes ratings greater than or equal to 6 (63.0%)
sum(na.omit(RottenTomatoes) >= 60)

## [1] 3252

Platforms by Ratings

#plot of imdb rating by platform
imdbPlat <- plot_ly(data=imdbNA, y = ~IMDb[Netflix == 'Yes'], x = ~(Netflix=c("Yes")), type = 'box', name = 'Netflix', marker = list(color = "firebrick"), color=I("firebrick"), text = ~paste("Movie: ", imdbNA$Title[Netflix == "Yes"], '<br>Year:', imdbNA$Year[Netflix=="Yes"])) %>%
    add_trace(y = ~IMDb[Hulu == 'Yes'], x = ~(Hulu=c("Yes")),  name = 'Hulu', marker = list(color = "#00EE76"), color=I("#00EE76"), text = ~paste("Movie: ", imdbNA$Title[Hulu == "Yes"], '<br>Year:', imdbNA$Year[Hulu=="Yes"])) %>%
    add_trace(y = ~IMDb[PrimeVideo == 'Yes'], x = ~(PrimeVideo=c("Yes")), name = 'Prime Video', marker = list(color = "#000033"), color=I("#000033"), text = ~paste("Movie: ", imdbNA$Title[PrimeVideo == "Yes"], '<br>Year:', imdbNA$Year[PrimeVideo=="Yes"])) %>%
    add_trace(y = ~IMDb[Disney == 'Yes'], x = ~(Disney=c("Yes")), name = 'Disney+', marker = list(color = "#0A47CC"), color=I("#0A47CC"), text = ~paste("Movie: ", imdbNA$Title[Disney == "Yes"], '<br>Year:', imdbNA$Year[Disney=="Yes"])) %>%
    layout(title = "IMDb Ratings for Each Platform",
                        yaxis = list(title = 'IMDb Rating'), 
                        xaxis = list(title = 'Platform',tickvals = c()),
                        boxmode = "group",
                        boxmean=TRUE,
                        legend = list(title = list(text = "Platform")))

#plot of Rotten tomatoes rating by platform
rotPlat <- plot_ly(data=rotNA, y = ~RottenTomatoes[Netflix == 'Yes'], x = ~(Netflix=c("Yes")), type = 'box', name = 'Netflix', marker = list(color = "firebrick"), color=I("firebrick"), text = ~paste("Movie: ", rotNA$Title[Netflix == "Yes"], '<br>Year:', rotNA$Year[Netflix=="Yes"])) %>%
    add_trace(y = ~RottenTomatoes[Hulu == 'Yes'], x = ~(Hulu=c("Yes")),  name = 'Hulu', marker = list(color = "#00EE76"), color=I("#00EE76"), text = ~paste("Movie: ", rotNA$Title[Hulu == "Yes"], '<br>Year:', rotNA$Year[Hulu=="Yes"])) %>%
    add_trace(y = ~RottenTomatoes[PrimeVideo == 'Yes'], x = ~(PrimeVideo=c("Yes")), name = 'Prime Video', marker = list(color = "#000033"), color=I("#000033"), text = ~paste("Movie: ", rotNA$Title[PrimeVideo == "Yes"], '<br>Year:', rotNA$Year[PrimeVideo=="Yes"])) %>%
    add_trace(y = ~RottenTomatoes[Disney == 'Yes'], x = ~(Disney=c("Yes")), name = 'Disney+', marker = list(color = "#0A47CC"), color=I("#0A47CC"), text = ~paste("Movie: ", rotNA$Title[Disney == "Yes"], '<br>Year:', rotNA$Year[Disney=="Yes"])) %>%
    layout(title = "Rotten Tomatoes Ratings for Each Platform",
                        yaxis = list(title = 'Rotten Tomatoes Rating'), 
                        xaxis = list(title = 'Platform',tickvals = c()),
                        boxmode = "group",
                        boxmean=TRUE,
                        legend = list(title = list(text = "Platform")))
imdbPlat #imdb plot

## Warning: 'layout' objects don't have these attributes: 'boxmode', 'boxmean'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'

rotPlat  #rotten tomatoes plot

## Warning: 'layout' objects don't have these attributes: 'boxmode', 'boxmean'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'

Correlation between IMDb Rating and Rotten Tomatoes Percentage

#data set without missing rating variables
ratingNA <- movdata[complete.cases(movdata$RottenTomatoes, movdata$IMDb),] #5156 observations
#for regression line
fit <- ratingNA %>% lm(RottenTomatoes ~ IMDb,.) %>% fitted.values

#Scatter plot of IMDb vs RottenTomatoes with Age
rateAgePlot <- plot_ly(ratingNA) %>%
  add_trace(x = ~IMDb, y = ~RottenTomatoes, type="scatter", mode = "markers", color=~Age, text = ~paste("Movie: ", Title, '<br>Year:', Year, '<br>Genre:', Genres, '<br>Runtime:', Runtime, '<br>Netflix:', Netflix, '<br>Hulu:', Hulu, '<br>Prime Video:', PrimeVideo, '<br>Disney+:', Disney)) %>%
  add_lines(x=~IMDb, y=fit, mode = "lines",showlegend=FALSE) %>%
  layout(title = "IMDb Rating vs. Rotten Tomatoes Percentage (with Age)", 
         xaxis = list(title = "IMDb Rating", range=c(0,10)), 
         yaxis = list(title = "Rotten Tomatoes Percentage"), range=c(0,100))
rateAgePlot

## Warning: 'layout' objects don't have these attributes: 'range'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'

#Correlation test between IMDb and Rotten Tomatoes Ratings
cor.test(RottenTomatoes, IMDb) 

## 
##  Pearson's product-moment correlation
## 
## data:  RottenTomatoes and IMDb
## t = 56.186, df = 5154, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5991028 0.6329684
## sample estimates:
##       cor 
## 0.6163205

#p-value is less than 0.05, so correlation is significant
#Correlation coefficient: 0.616

Platforms of “High Rating” Movies

#Create subset with top Rotten Tomatoes ratings from 80-100% or IMDb ratings from 8-10
#uses data without missing values since we are counting by platform
topRatings <- movdata[which(movdata$RottenTomatoes >= 80 | movdata$IMDb >= 8.0),] #2538 Movies
#Since data relies on there being a value in either IMDB or RottenTomatoes, then we need new count values that counts the Movies in each platform when they don't have missings in both IMDb and RottenTomatoes
#Create dataset that includes values if not missing in both ratings
library(dplyr)
ratingNDNA <- full_join(imdbNA, rotNA) #16175 obs - 569 missing - 3.3%

## Joining, by = c("ID", "Title", "Year", "Age", "IMDb", "RottenTomatoes", "Netflix", "Hulu", "PrimeVideo", "Disney", "Directors", "Genres", "Country", "Language", "Runtime")

#count how many of each platform are in this dataset
platmatNRA <- sapply(X = ratingNDNA[7:10], FUN = table)
count_noNRA <- platmatNRA[1:1, 1:4] 
count_yesNRA <- platmatNRA[2:2, 1:4]
platNA <- data.frame(platforms, count_yesNRA, count_noNRA) #reorganization of freq table

#create dataset that includes the counts of the number of Movies in or not in each platform with high ratings
platmatTop <- sapply(X = topRatings[7:10], FUN = table)
count_noTop <- platmatTop[1:1, 1:4] 
count_yesTop <- platmatTop[2:2, 1:4]
platdataTop <- data.frame(platforms, count_yesTop, count_noTop)

#Plot of how many Movies with high ratings are in or not in each platform
platTop <- plot_ly(platdataTop, x = ~platforms, y = ~count_yesTop, type = 'bar', name = 'Yes') %>%
  add_trace(y = ~count_noTop, name = 'No') %>%
  layout(title = "Number of Movies in Each Streaming Platform with High Ratings",
                        yaxis = list(title = 'Number of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the movie in the platform?")))

#Calculate percent of Movies that have a high rating
platTopPercY <- (count_yesTop / count_yesNRA)*100
platTopPercN <- (count_noTop / count_noNRA)*100
#Combine into percents and counts into dataset platdatatop
platdataTop <- cbind(platdataTop, platTopPercY)
platdataTop <- cbind(platdataTop, platTopPercN)
platdataTop <- cbind(platdataTop, count_yesNRA)
platdataTop <- cbind(platdataTop, count_noNRA)

#Percentage is of how many higher rating movies are in/not in the platform over the total number of movies in/not in the platform
#Ex: 32.6% of movies in Hulu have a high rating
#Ex: 24.0% of movies in Disney+ have a high rating
#Ex: 20.5% of movies in Netflix have a high rating

#Ex: 12.9% of movies in Prime Video have a high rating
#Ex: 23.4% of movies not in Prime Video have a high rating
platTopPercP <- plot_ly(platdataTop, x = ~platforms, y = ~platTopPercY, type = 'bar', name = 'Yes') %>%
    add_trace(y = ~platTopPercN, name = 'No') %>%
    layout(title = "Percent of Movies in Each Streaming Platform with High Ratings",
                        yaxis = list(title = 'Percent of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the movie in the platform?")))

platTop #plot top counts

platTopPercP #plot top percents

Relation of Different Platforms

# create alluvial diagram
library(ggalluvial)
library(ggfittext)
Nalluv <- ggplot(movdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) + 
  geom_alluvium(aes(fill=Netflix), knot.pos=0) +
  geom_stratum(alpha=.5) + 
  geom_text(stat = "stratum", aes(label= after_stat(stratum))) + 
  scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
  labs(title = "Movies of Streaming Platforms", subtitle = "stratified by Netflix", 
       y = "Frequency") + 
  theme_minimal()

Halluv <- ggplot(movdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) + 
  geom_alluvium(aes(fill=Hulu), knot.pos=0) +
  geom_stratum(alpha=.5) + 
  geom_text(stat = "stratum", aes(label= after_stat(stratum))) + 
  scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
  labs(title = "Movies of Streaming Platforms", subtitle = "stratified by Hulu", 
       y = "Frequency") + 
  theme_minimal()

Palluv <- ggplot(movdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) + 
  geom_alluvium(aes(fill=PrimeVideo), knot.pos=0) +
  geom_stratum(alpha=.5) + 
  geom_text(stat = "stratum", aes(label= after_stat(stratum))) + 
  scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
  labs(title = "Movies of Streaming Platforms", subtitle = "stratified by Prime Video", 
       y = "Frequency") + 
  theme_minimal()

Dalluv <- ggplot(movdata, aes(axis1 = Netflix, axis2 = Hulu, axis3 = PrimeVideo, axis4 = Disney, y = stat(count))) + 
  geom_alluvium(aes(fill=Disney), knot.pos=0) +
  geom_stratum(alpha=.5) + 
  geom_text(stat = "stratum", aes(label= after_stat(stratum))) + 
  scale_x_discrete(limits = c("Netflix", "Hulu", "Prime Video", "Disney"), expand = c(.1, .1)) +
  labs(title = "Movies of Streaming Platforms", subtitle = "stratified by Disney+", 
       y = "Frequency") + 
  theme_minimal()

Nalluv

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

Halluv

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

Palluv

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

Dalluv

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

## Warning in to_lodes_form(data = data, axes = axis_ind, discern =
## params$discern): Some strata appear at multiple axes.

#There are no movies that are in all 4 platforms
plat4 <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "Yes" & movdata$Disney == "Yes"),]

#How many movies are on 3 platforms
#Not in Disney - 6
plat3NHP_D <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "Yes" & movdata$Disney == "No"),]
#Not in Hulu - 1
plat3N_HPD <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "No" & movdata$PrimeVideo == "Yes" & movdata$Disney == "Yes"),]
#Not in Netflix - 2
plat3_NHPD <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "Yes" & movdata$Disney == "Yes"),]
#Not in Prime - 1
plat3NH_PD <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "No" & movdata$Disney == "Yes"),]
#Combine into 1 dataset - 7 (0.04% of data set)
plat3 <- rbind(plat3NHP_D, plat3NH_PD)

#How many movies are on 1 platforms
#Only in Netflix - 3188 - 89.6% of Netflix
plat1N <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "No" & movdata$PrimeVideo == "No" & movdata$Disney == "No"),]
#Only in Hulu - 639 - 70.8% of Hulu
plat1H <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "No" & movdata$Disney == "No"),]
#Only in Prime - 11748 - 95.1% of Prime Video
plat1P <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "No" & movdata$PrimeVideo == "Yes" & movdata$Disney == "No"),]
#Only in Disney - 532 - 94.3& of Disney+
plat1D <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "No" & movdata$PrimeVideo == "No" & movdata$Disney == "Yes"),]
#Combine into 1 dataset - 16117 obs (96.3% of data set)
plat1 <- rbind(plat1N, plat1H, plat1P, plat1D)

#How many movies are on 2 platforms
#In Netflix and Hulu - 18
plat2NH <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "No" & movdata$Disney == "No"),]
#In Netflix and Prime - 338
plat2NP <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "No" & movdata$PrimeVideo == "Yes" & movdata$Disney == "No"),]
#In Netflix and Disney - 8
plat2ND <- movdata[which(movdata$Netflix == "Yes" & movdata$Hulu == "No" & movdata$PrimeVideo == "No" & movdata$Disney == "Yes"),]
#In Hulu and Prime - 233
plat2HP <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "Yes" & movdata$Disney == "No"),]
#In Hulu and Disney - 4
plat2HD <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "Yes" & movdata$PrimeVideo == "No" & movdata$Disney == "Yes"),]
#In Prime and Disney - 16
plat2PD <- movdata[which(movdata$Netflix == "No" & movdata$Hulu == "No" & movdata$PrimeVideo == "Yes" & movdata$Disney == "Yes"),]
#Combine into 1 dataset - 617 obs (3.7% of data set)
plat2 <- rbind(plat2NH, plat2NP, plat2ND, plat2HP, plat2HD, plat2PD)

Blockbusters in each Platform

#import blockbuster dataset
#Blockbuster dataset:
# Top 10 blockbusters every year starting in 1975-2018
# 437 movies
bbdata <- read_csv("blockbusters.csv", 
    col_types = cols(rank_in_year = col_integer()))
#change variables to factor
bbdata$rating <- as.factor(bbdata$rating)
bbdata$studio <- as.factor(bbdata$studio)
bbdata$imdb_rating <- as.numeric(bbdata$imdb_rating)

#2019 data scraped from https://www.boxofficemojo.com/year/2019/
#bbdata now has 447 movies
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 1, "PG-13", "Walt Disney Pictures", "Avengers: Endgame", "$858,373,000", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 2, "PG", "Walt Disney Pictures", "The Lion King", "$543,638,043", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 3, "G", "Walt Disney Pictures", "Toy Story 4", "$434,038,008", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 4, "PG", "Walt Disney Pictures", "Frozen II", "$430,144,682", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 5, "PG-13", "Walt Disney Pictures", "Captain Marvel", "$426,829,839", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 6, "PG-13", "Walt Disney Pictures", "Star Wars: The Rise of Skywalker", "$390,706,234", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 7, "PG-13", "Sony Pictures", "Spider-Man: Far from Home", "$390,532,085", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 8, "PG", "Walt Disney Pictures", "Aladdin", "$355,559,216", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 9, "R", "Warner Bros", "Joker", "$333,772,511", 2019))
bbdata <- rbind(bbdata, c(NA, NA, NA, NA, NA, 10, "R", "Warner Bros", "It Chapter Two", "$211,593,228", 2019))

names(bbdata)[9] <- "Title" #capitalize title column
#merge platform and blockbuster data sets to determine which platforms have blockbusters
bbinplat <- merge(movdata, bbdata, by = "Title")
bbinplat <- as.data.frame(bbinplat)
#check that the years for each data set match
#yearsbb <- plot_ly(bbinplat, x = ~year, y = ~Year, type="scatter", mode = "markers",
                    #text = ~paste("Movie: ", Title)) %>%
  #layout(title = "Years of Blockbuster Movies in Platforms", 
         #xaxis = list(title = "Blockbuster Movie Year"), 
         #yaxis = list(title = "Platform Movie Year"),
         #showlegend = FALSE)
#yearsbb

#remove 17 movies:
#The Lion King 2019, Aladdin 2019, Joker, The Amityville Horror, Annie, Footloose, Back to School, The Little Mermaid, A Star is Born 1937, The Nutty Professor, The Hunchback of Notre Dame, Alice in Wonderland, Suicide Squad, Cinderella, The Jungle Book, Inside Out
bbinplat <- bbinplat[-c(9, 109, 60, 94, 11, 39, 19, 110, 111, 5, 116, 103, 10, 89, 32, 106, 56), ]

#number of movies in each platform
#No missing data in each platform
platMatbb <- sapply(X = bbinplat[7:10], FUN = table) #frequency table
platforms <- c("Netflix", "Hulu", "Prime Video", "Disney+") #names of platforms
count_nobb <- platMatbb[1:1, 1:4] #extracts frequencies of "No" values
count_yesbb <- platMatbb[2:2, 1:4] #extracts frequencies of "Yes" values
count_yesNbb <- count_yesbb[1] #total number of movies in Netflix - 3560 (21.3%)
count_yesHbb <- count_yesbb[2] #total number of movies in Hulu - 903 (5.4%)
count_yesPbb <- count_yesbb[3] #total number of movies in Prime Video - 12354 (73.8%)
count_yesDbb <- count_yesbb[4] #total number of movies in Disney - 564 (3.4%)
platdatabb <- data.frame(platforms, count_yesbb, count_nobb) #reorganization of freq table

#Plot number of blockbusters movies in each platform as a grouped bar chart
platbb <- plot_ly(platdatabb, x = ~platforms, y = ~count_yesbb, type = 'bar', name = 'Yes') %>%
  add_trace(y = ~count_nobb, name = 'No') %>%
  layout(title = "Number of Blockbuster Movies in Each Streaming Platform",
                        yaxis = list(title = 'Number of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the movie in the platform?")))

#Calculate percent of Movies that have are blockbusters
platbbPercY <- (count_yesbb / count_yes)*100
platbbPercN <- (count_nobb / count_no)*100
#Combine into percents and counts into dataset platdatatop
platdatabb <- cbind(platdatabb, platbbPercY)
platdatabb <- cbind(platdatabb, platbbPercN)
platdatabb <- cbind(platdatabb, count_yes)
platdatabb <- cbind(platdatabb, count_no)
platbbPercP <- plot_ly(platdatabb, x = ~platforms, y = ~platbbPercY, type = 'bar', name = 'Yes') %>%
    add_trace(y = ~platbbPercN, name = 'No') %>%
    layout(title = "Percent of Blockbuster Movies in Each Streaming Platform",
                        yaxis = list(title = 'Percent of Movies'), 
                        xaxis = list(title = 'Streaming Platform'),
                        barmode = 'group',
                        legend = list(title = list(text = "Is the blockbuster movie in the platform?")))

platbb #plot platbb

platbbPercP

Ratings of Blockbuster Movies on Platforms

ratingbbNA <- bbinplat[complete.cases(bbinplat$RottenTomatoes, bbinplat$IMDb),] #114 observations
fitbb <- ratingbbNA %>% lm(RottenTomatoes ~ IMDb,.) %>% fitted.values #for regression line
#Order rank
ratingbbNA$rank_in_year <- as.factor(ordered(ratingbbNA$rank_in_year, levels = c("1", "2", "3", "4", "5","6", "7", "8", "9", "10")))
#plot platform blockbusters imdb by rotten tomatoes, colored by rank
ratebbPlot <- plot_ly(ratingbbNA) %>%
  add_trace(x = ~IMDb, y = ~RottenTomatoes, type="scatter", mode = "markers", color=~reorder(rank_in_year, -rank_in_year), text = ~paste("Movie: ", Title, '<br>Year:', Year, '<br>Genre:', Genres, '<br>Runtime:', Runtime, '<br>Netflix:', Netflix, '<br>Hulu:', Hulu, '<br>Prime Video:', PrimeVideo, '<br>Disney+:', Disney, '<br>Rank:', rank_in_year)) %>%
  add_lines(x=~IMDb, y=fitbb, mode = "lines",showlegend=FALSE) %>%
  layout(title = "IMDb Rating vs. Rotten Tomatoes Percentage (with Blockbuster Rank)", 
         xaxis = list(title = "IMDb Rating", range=c(0,10)), 
         yaxis = list(title = "Rotten Tomatoes Percentage"), range=c(0,100))
ratebbPlot

## Warning in Ops.ordered(rank_in_year): '-' is not meaningful for ordered factors

## Warning: 'layout' objects don't have these attributes: 'range'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'

#Correlation test between IMDb and Rotten Tomatoes Ratings
cor.test(ratingbbNA$IMDb, ratingbbNA$RottenTomatoes) 

## 
##  Pearson's product-moment correlation
## 
## data:  ratingbbNA$IMDb and ratingbbNA$RottenTomatoes
## t = 11.494, df = 111, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6395115 0.8113945
## sample estimates:
##      cor 
## 0.737156

#p-value is less than 0.05, so correlation is significant
#Correlation coefficient: 0.737